and ready access to data and simulation tools have helped make Deep Reinforcement Learning one of the most powerful tools for dealing with control-driven dynamic systems today. From the design of ...

Model instability: RL models, especially when used with neural networks (as in deep reinforcement learning), can be unstable during training, requiring careful tuning of hyperparameters to avoid ...

DeepSeek AI, a prominent player in the large language model arena, has recently published a research paper detailing a new technique aimed at enhancing the scalability of general reward models (GRMs) ...

By categorizing and filtering user input, you can better focus on driving AI improvement. This iterative process—blending automation with human review—ensures AI learns from high-quality data, leading ...

DeepCoder-14B competes with frontier models like o3 and o1—and the weights, code, and optimization platform are open source.

The digital era has witnessed unprecedented technological advancements, with artificial intelligence emerging as one of the ...

That is when Barto and his then-Ph.D. student Sutton proposed reinforcement learning as a general problem-solving framework.

The review introduces a proposed two-layer reinforcement learning framework for distributed smart grid control. In this ...

In the ever-evolving world of artificial intelligence (AI), the ability to make effective decisions is a cornerstone of ...

This important study presents single-unit activity collected during model-based (MB) and model-free (MF) reinforcement learning in non-human primates. The dataset was carefully collected, and the ...